Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations

Ilustraciones y tablas

Autores:
Bautista Sánchez, Frank Jair
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/80194
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/80194
https://repositorio.unal.edu.co/
Palabra clave:
520 - Astronomía y ciencias afines
Planets
Planetas
Astronomy
Astronomía
Cosmic physics
Física cósmica
Bayesian inference
Planet population synthesis
Gaussian mixture model
Kernel density estimation
Formación planetaria
Mezcla gaussiana
Síntesis planetaria
Inferencia bayesiana
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_0919a88d963080d1ac836aac806ffbb4
oai_identifier_str oai:repositorio.unal.edu.co:unal/80194
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
dc.title.translated.spa.fl_str_mv Restricciones bayesianas sobre las propiedades observables de los sistemas exoplanetarios utilizando simulaciones de síntesis de población planetaria
title Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
spellingShingle Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
520 - Astronomía y ciencias afines
Planets
Planetas
Astronomy
Astronomía
Cosmic physics
Física cósmica
Bayesian inference
Planet population synthesis
Gaussian mixture model
Kernel density estimation
Formación planetaria
Mezcla gaussiana
Síntesis planetaria
Inferencia bayesiana
title_short Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
title_full Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
title_fullStr Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
title_full_unstemmed Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
title_sort Bayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulations
dc.creator.fl_str_mv Bautista Sánchez, Frank Jair
dc.contributor.advisor.none.fl_str_mv Chaparro Molano, Germán
Vargas Domínguez, Santiago
dc.contributor.author.none.fl_str_mv Bautista Sánchez, Frank Jair
dc.subject.ddc.spa.fl_str_mv 520 - Astronomía y ciencias afines
topic 520 - Astronomía y ciencias afines
Planets
Planetas
Astronomy
Astronomía
Cosmic physics
Física cósmica
Bayesian inference
Planet population synthesis
Gaussian mixture model
Kernel density estimation
Formación planetaria
Mezcla gaussiana
Síntesis planetaria
Inferencia bayesiana
dc.subject.lemb.none.fl_str_mv Planets
Planetas
Astronomy
Astronomía
Cosmic physics
Física cósmica
dc.subject.proposal.eng.fl_str_mv Bayesian inference
Planet population synthesis
Gaussian mixture model
Kernel density estimation
dc.subject.proposal.spa.fl_str_mv Formación planetaria
Mezcla gaussiana
Síntesis planetaria
Inferencia bayesiana
description Ilustraciones y tablas
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-09-15T12:53:39Z
dc.date.available.none.fl_str_mv 2021-09-15T12:53:39Z
dc.date.issued.none.fl_str_mv 2021-08-18
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/80194
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/80194
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.format.extent.spa.fl_str_mv xii, 92 páginas
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dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ciencias - Maestría en Ciencias - Física
dc.publisher.department.spa.fl_str_mv Departamento de Física
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
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spelling Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Chaparro Molano, Germán08c4d569704ab20c98aa0e3806591182Vargas Domínguez, Santiagoe9d25dbef9da29422adab51ba9732510600Bautista Sánchez, Frank Jair16ca30ee5510a2249dd9adaa8366b8642021-09-15T12:53:39Z2021-09-15T12:53:39Z2021-08-18https://repositorio.unal.edu.co/handle/unal/80194Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones y tablasRecent advances in exoplanet observations have led to the discovery of nearly 4400 planets in more than 3000 planetary systems. However, many physical properties of these systems remain largely unknown due to observational biases and stochasticity in their formation processes. We address current limitations of exoplanet classification by formulating a new, robust classification scheme based on the first moment of planet mass vs. semi-major axis distribution using Gaussian Mixture Models (GMM). We validate our method via Approximate Bayesian Computation and Information Criteria, which shows that it is robust with uncertainties measurement and adaptable to new observations. Our scheme yields four planetary system classes: Sub-Mercurial systems, Venusian systems, Solar-like systems, and Periphery systems. We also propose a new method for compensating observational biases while addressing stochasticity in star and planet formation. To this end, we develop a Bayesian probabilistic formalism in which we take priors from observed planetary systems and marginalize them over synthetic likelihood functions. It allows us to estimate probability distribution functions for variables of interest, such as the total number of planets, total planetary mass, rocky planetary mass, center of mass, among others. We generate our synthetic likelihood functions from a multivariate Kernel Density Estimation (KDE) model based on the results of a Monte Carlo simulation of 1200 planet population synthesis models, drawn from observational priors obtained from the literature. We assess the performance of the kernel parameter choice using cross-validation. Therefore, we got probability distributions of physical variables of interest for ten observed systems using data from public catalogues. For the selected systems, we infer that they had initial disks with masses around 0.1 M⊙ ± 0.01 M⊙, their centers of mass are located around 5 AU ± 2 AU, and they should have around seven more planets than are currently observed. We also conclude that the number of rocky planets significantly contributes to the total number of planets, so we expect to find more rocky planets in future observations. Our formalism allows getting the probability distributions of exoplanetary systems unobserved or with biased properties. It will help steering future astronomical surveys and motivating further questions of observed planetary systems.Los avances recientes en las observaciones de exoplanetas han llevado al descubrimiento de casi 4400 planetas en más de 3000 sistemas planetarios. Sin embargo, muchas propiedades físicas de estos sistemas siguen siendo en gran parte desconocidas debido a sesgos observacionales y estocasticidad en sus procesos de formación. Abordamos las limitaciones actuales de la clasificación de exoplanetas mediante la formulación de un nuevo y robusto esquema de clasificación basado en el primer momento de la masa del planeta frente a la distribución del eje semi-mayor utilizando modelos de mezcla gaussianos (GMM). Validamos nuestro método mediante criterios de información y cálculo bayesianos aproximados, lo que demuestra que es robusto con la medición de incertidumbres y adaptable a nuevas observaciones. Nuestro esquema produce cuatro clases de sistemas planetarios: sistemas Sub-Mercurianos, sistemas Venusianos, sistemas similares al solar y sistemas periféricos. También proponemos un nuevo método para compensar los sesgos de observación al abordar la estocasticidad en la formación de estrellas y planetas. Con este fin, desarrollamos un formalismo probabilístico bayesiano en el que tomamos información previa de los sistemas planetarios observados y los marginalizamos sobre las funciones de verosimilitud sintéticas. Lo que nos permite estimar funciones de distribución de probabilidad para variables de interés, como el número total de planetas, la masa total planetaria, la masa planetaria rocosa, centro de masa, entre otras. Generamos nuestras funciones de verosimilitud sintéticas a partir de un modelo multivariado de estimación de densidad de núcleo (KDE) basado en los resultados de una simulación de Monte Carlo de 1200 modelos de síntesis de población planetaria, extraídos de observaciones previas obtenidas de la literatura. Evaluamos el rendimiento de la elección del parámetro de una función núcleo (Kernel) mediante validación cruzada. De esta manera, obtuvimos distribuciones de probabilidad de variables físicas de interés para diez sistemas observados utilizando datos de catálogos públicos. Para los sistemas seleccionados, inferimos que tenían discos iniciales con masas alrededor 0.1 M⊙ ± 0.01 M⊙, sus centros de masa se ubican alrededor de 5 AU ± 2 AU, y deberían tener alrededor de siete planetas más de los que se observan actualmente. También concluimos que el número de planetas rocosos contribuye significativamente al número total de planetas, por lo que esperamos encontrar más planetas rocosos en futuras observaciones. Nuestro formalismo permite obtener las distribuciones de probabilidad de sistemas exoplanetarios no observados o con propiedades sesgadas. Este formalismo ayudará a dirigir los estudios astronómicos futuros y a motivar más preguntas sobre los sistemas planetarios observados. (Texto tomado de la fuente).Incluye anexosMaestríaMagíster en Ciencias - FísicaFormación Eexoplanetaria - Sintesis planetariaxii, 92 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - FísicaDepartamento de FísicaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá520 - Astronomía y ciencias afinesPlanetsPlanetasAstronomyAstronomíaCosmic physicsFísica cósmicaBayesian inferencePlanet population synthesisGaussian mixture modelKernel density estimationFormación planetariaMezcla gaussianaSíntesis planetariaInferencia bayesianaBayesian constraints on observable properties of exoplanetary systems using planet population synthesis simulationsRestricciones bayesianas sobre las propiedades observables de los sistemas exoplanetarios utilizando simulaciones de síntesis de población planetariaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAkaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716–723.Alibert, Y., Mordasini, C., and Benz, W. (2010). Extrasolar planet population synthesis. A&A, 526:A63.Armitage, P. J. (2009). Astrophysics of Planet Formation. Cambridge University Press.Bashi, D., Helled, R., and Zucker, S. (2018). A quantitative comparison of exoplanet catalogs. Geosciences, 8:325.Benz, W., Ida, S., Alibert, Y., Lin, D., and Mordasini, C. (2014). Planet population synthesis. In Protostars and Planets VI, page 691.Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.Bonanos, A. Z., Yang, M., Sokolovsky, K. V., Gavras, P., Hatzidimitriou, D., Bellas-Velidis, I., Kakaletris, G., Lennon, D. J., Nota, A., White, R. L., Whitmore, B. C., Anastasiou, K. 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